2011 by James L. Cornette, Ralph A. Ackerman, Iowa State University.
Includes mathematical modeling and difference and differential equations, which closely follow, and extend the elements of calculus. Introduces mathematical modeling in which students write descriptions of some observed processes and from these descriptions derive first order linear difference equations whose solutions can be compared with the observed data. In chapters in which the derivatives of algebraic, exponential, or trigonometric functions are defined, biologically motivated differential equations and their solutions are included. The chapter on partial derivatives includes a section on the diffusion partial differential equation. There are two chapters on non-linear difference equations and on systems of two difference equations and two chapters on differential equations and on systems of differential equation.
Updated 2021 by Manolis Kellis et al.
An MIT Open course. Covers the algorithmic and machine learning foundations of computational biology combining theory with practice. Covers both foundational topics in computational biology, and current research frontiers, fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.
